xiaoyi-tyut Goto Github PK

aiaihealthcare.github.io

AiAi code-charity project for Cancer and Tuberculosis screening in Radiology Chest X-Rays (PA - CXR) using Deep Learning powered CAD algorithms. Come join our all-volunteer team to improve the healthcare in emerging nations!

aishow

AI展示系统，后端采用django+pytorch框架,前端采用react框架

azurecustomvisioncovid-19

As we continue to face the rapid increase in confirmed Coronavirus cases in India, we intend to create a robust pneumonia detection platform for COVID-19. The purpose is to assist doctors and radiologists in recognizing patients who are infected.The system is built to automatically detect high-risk patients with pneumonia or other chest diseases that will then send the information to doctors and health care professionals via secure network. With that information, the doctors are then able to make decisions and provide a treatment plan for the diagnosis and treatment. Any x-ray images taken for patients will automatically be uploaded to our system. Our system will then scan the images to determine whether the patients are infected. If the test is positive, the doctors will receive an e-Alert (on computer or mobile phone) that contains the original scans and detection results. Fast and Cheap Computer aided Diagnosis will prove extremely beneficial for people in rural India where there is acute paucity of trained radiologists and Doctors per capita. We will create an end-to-end Deep learning solution for Chest X-ray diagnosis. It will be a cost-effective solution for rural population with inadequate access to diagnostic imaging specialists.

bank_interview

:bank: 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）

bigbigan

Runway Port of BigBiGAN from the paper "Large Scale Adversarial Representation Learning"

bigbigan-for-covid-19

Code and resorces of the study "End-to-end automatic detection of the Coronavirus disease 2019 (COVID-19) based on CT image"

bloodcell-identification-tfjs-client

Blood Cell Identification system is a web app built using React and Tensorflow js, The CNN model trained to classify blood cell images using python and is ported to Browser Client using tensorflow model converter.

bnk48-face-recognition

Facial Recognition Web App using React.js and face-api.js

clinical-grade-computational-pathology-using-weakly-supervised-deep-learning-on-whole-slide-images

首个临床级别的 AI 系统可用源码

clone_viewer

ohif/[email protected], meteor version

covid-ct

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

ctai

基于深度学习的肿瘤辅助诊断系统，以图像分割为核心，利用人工智能完成肿瘤区域的识别勾画并提供肿瘤区域的特征来辅助医生进行诊断。有完整的模型构建、后端架设和前端访问功能。

deep-learning-browser

Official repository of the book "Deep learning in the browser" published by Bleeding Edge Press August 2018. Find here all of the source code of the demos!

disease_prediction_system

The project is a Disease Prediction Application Based on Medical Imaging implementing Deep Learning and Python. The project will include the diagnosis for Breast Cancer, Malaria and Parkinson's disease.

dwv

DICOM Web Viewer: open source zero footprint medical image viewer.

easylung

从X光胸片医疗影像中AI智能识别肺炎及病原体种类的辅助诊断平台，包含网页、微信小程序、APP。

eccv2020-code

CVPR 2020 论文开源项目合集，同时欢迎各位大佬提交issue，分享CVPR 2020开源项目

electrify

Package your Meteor apps with Electron, and butter

face-api-demo-vue

Face-api Vue简单使用案例 face-api-demo-vue

faster-rcnn-tensorflow-python3

Tensorflow Faster R-CNN for Windows/Linux and Python 3 (3.5/3.6/3.7)

faster-rcnn_tf

Faster-RCNN in Tensorflow

graduation-electron

imgsquash

Simple image compression full website code written in node, react and next.js framework. Easy to deploy as a microservice.

lidc_nodule_detection

lidc nodule detection with CNN and LSTM network

lung

for the paper of 3D Inception CNN For Automatic Lung Nodule Detection

lung-cancer-detection

Detecting malignant lung nodules using deep learning.

lung-nodule-detection-luna-16

Lung nodule detection- LUNA 16

lung-nodules-detection

肺结节智能检测项目

lung_cancer_detection

lung_nodule_analysis

Collection of programs and scripts used in Computer Vision (ECE5470) final project at Cornell University